| 2024 | Independent Learning in Constrained Markov Potential Games. | Philip Jordan, Anas Barakat, Niao He |
| 2024 | Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees. | Alexia Jolicoeur-Martineau, Kilian Fatras, Tal Kachman |
| 2024 | Subsampling Error in Stochastic Gradient Langevin Diffusions. | Kexin Jin, Chenguang Liu, Jonas Latz |
| 2024 | Feasible Q-Learning for Average Reward Reinforcement Learning. | Ying Jin, Ramki Gummadi, Zhengyuan Zhou, Jose H. Blanchet |
| 2024 | Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate. | Ruichen Jiang, Parameswaran Raman, Shoham Sabach, Aryan Mokhtari, Mingyi Hong, Volkan Cevher |
| 2024 | FedFisher: Leveraging Fisher Information for One-Shot Federated Learning. | Divyansh Jhunjhunwala, Shiqiang Wang, Gauri Joshi |
| 2024 | On-Demand Federated Learning for Arbitrary Target Class Distributions. | Isu Jeong, Seulki Lee |
| 2024 | Exploration via linearly perturbed loss minimisation. | David Janz, Shuai Liu, Alex Ayoub, Csaba Szepesvri |
| 2024 | Quantifying intrinsic causal contributions via structure preserving interventions. | Dominik Janzing, Patrick Blbaum, Atalanti-Anastasia Mastakouri, Philipp Michael Faller, Lenon Minorics, Kailash Budhathoki |
| 2024 | Reparameterized Variational Rejection Sampling. | Martin Jankowiak, Du Phan |
| 2024 | Achieving Fairness through Separability: A Unified Framework for Fair Representation Learning. | Taeuk Jang, Hongchang Gao, Pengyi Shi, Xiaoqian Wang |
| 2024 | Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems. | Neharika Jali, Guannan Qu, Weina Wang, Gauri Joshi |
| 2024 | From Data Imputation to Data Cleaning - Automated Cleaning of Tabular Data Improves Downstream Predictive Performance. | Sebastian Jger, Felix Biessmann |
| 2024 | Provable local learning rule by expert aggregation for a Hawkes network. | Sophie Jaffard, Samuel Vaiter, Alexandre Muzy, Patricia Reynaud-Bouret |
| 2024 | A Bayesian Learning Algorithm for Unknown Zero-sum Stochastic Games with an Arbitrary Opponent. | Mehdi Jafarnia-Jahromi, Rahul Jain, Ashutosh Nayyar |
| 2024 | Risk Seeking Bayesian Optimization under Uncertainty for Obtaining Extremum. | Shogo Iwazaki, Tomohiko Tanabe, Mitsuru Irie, Shion Takeno, Yu Inatsu |
| 2024 | AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms. | Rustem Islamov, Mher Safaryan, Dan Alistarh |
| 2024 | Adaptive Compression in Federated Learning via Side Information. | Berivan Isik, Francesco Pase, Deniz Gndz, Sanmi Koyejo, Tsachy Weissman, Michele Zorzi |
| 2024 | Learning Latent Partial Matchings with Gumbel-IPF Networks. | Hedda Cohen Indelman, Tamir Hazan |
| 2024 | Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty. | Yu Inatsu, Shion Takeno, Hiroyuki Hanada, Kazuki Iwata, Ichiro Takeuchi |
| 2024 | Understanding Inverse Scaling and Emergence in Multitask Representation Learning. | Muhammed Emrullah Ildiz, Zhe Zhao, Samet Oymak |
| 2024 | Learning Dynamics in Linear VAE: Posterior Collapse Threshold, Superfluous Latent Space Pitfalls, and Speedup with KL Annealing. | Yuma Ichikawa, Koji Hukushima |
| 2024 | End-to-end Feature Selection Approach for Learning Skinny Trees. | Shibal Ibrahim, Kayhan Behdin, Rahul Mazumder |
| 2024 | Adaptive Experiment Design with Synthetic Controls. | Alihan Hyk, Zhaozhi Qian, Mihaela van der Schaar |
| 2024 | DAGnosis: Localized Identification of Data Inconsistencies using Structures. | Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabb, Zhaozhi Qian, Mihaela van der Schaar |